Overview

Brought to you by YData

Dataset statistics

Number of variables23
Number of observations4617600
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.2 GiB
Average record size in memory968.9 B

Variable types

DateTime1
Numeric4
Text16
Categorical2

Alerts

units is highly skewed (γ1 = 60.0807121) Skewed
units has 4498904 (97.4%) zeros Zeros

Reproduction

Analysis started2025-05-16 22:34:54.828430
Analysis finished2025-05-16 22:36:42.097665
Duration1 minute and 47.27 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

date
Date

Distinct1034
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size35.2 MiB
Minimum2012-01-01 00:00:00
Maximum2014-10-31 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-05-17T00:36:42.140903image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-17T00:36:42.195674image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

store_nbr
Real number (ℝ)

Distinct45
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.091082
Minimum1
Maximum45
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size35.2 MiB
2025-05-17T00:36:42.249636image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q112
median23
Q334
95-th percentile43
Maximum45
Range44
Interquartile range (IQR)22

Descriptive statistics

Standard deviation12.952808
Coefficient of variation (CV)0.56094417
Kurtosis-1.1973048
Mean23.091082
Median Absolute Deviation (MAD)11
Skewness-0.010531251
Sum1.0662538 × 108
Variance167.77523
MonotonicityNot monotonic
2025-05-17T00:36:42.301498image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
40 112221
 
2.4%
37 112221
 
2.4%
25 112221
 
2.4%
32 112221
 
2.4%
15 112221
 
2.4%
39 109113
 
2.4%
36 107004
 
2.3%
4 106560
 
2.3%
24 106560
 
2.3%
14 105672
 
2.3%
Other values (35) 3521586
76.3%
ValueCountFrequency (%)
1 103119
2.2%
2 97125
2.1%
3 99456
2.2%
4 106560
2.3%
5 98568
2.1%
6 97125
2.1%
7 102786
2.2%
8 102120
2.2%
9 105117
2.3%
10 98568
2.1%
ValueCountFrequency (%)
45 105672
2.3%
44 98568
2.1%
43 101121
2.2%
42 97125
2.1%
41 98568
2.1%
40 112221
2.4%
39 109113
2.4%
38 97125
2.1%
37 112221
2.4%
36 107004
2.3%

item_nbr
Real number (ℝ)

Distinct111
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56
Minimum1
Maximum111
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size35.2 MiB
2025-05-17T00:36:42.355194image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q128
median56
Q384
95-th percentile106
Maximum111
Range110
Interquartile range (IQR)56

Descriptive statistics

Standard deviation32.041643
Coefficient of variation (CV)0.5721722
Kurtosis-1.2001948
Mean56
Median Absolute Deviation (MAD)28
Skewness0
Sum2.585856 × 108
Variance1026.6669
MonotonicityNot monotonic
2025-05-17T00:36:42.411554image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 41600
 
0.9%
71 41600
 
0.9%
82 41600
 
0.9%
81 41600
 
0.9%
80 41600
 
0.9%
79 41600
 
0.9%
78 41600
 
0.9%
77 41600
 
0.9%
76 41600
 
0.9%
75 41600
 
0.9%
Other values (101) 4201600
91.0%
ValueCountFrequency (%)
1 41600
0.9%
2 41600
0.9%
3 41600
0.9%
4 41600
0.9%
5 41600
0.9%
6 41600
0.9%
7 41600
0.9%
8 41600
0.9%
9 41600
0.9%
10 41600
0.9%
ValueCountFrequency (%)
111 41600
0.9%
110 41600
0.9%
109 41600
0.9%
108 41600
0.9%
107 41600
0.9%
106 41600
0.9%
105 41600
0.9%
104 41600
0.9%
103 41600
0.9%
102 41600
0.9%

units
Real number (ℝ)

Skewed  Zeros 

Distinct394
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.98687565
Minimum0
Maximum5568
Zeros4498904
Zeros (%)97.4%
Negative0
Negative (%)0.0%
Memory size35.2 MiB
2025-05-17T00:36:42.464695image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum5568
Range5568
Interquartile range (IQR)0

Descriptive statistics

Standard deviation9.875798
Coefficient of variation (CV)10.007135
Kurtosis25038.775
Mean0.98687565
Median Absolute Deviation (MAD)0
Skewness60.080712
Sum4556997
Variance97.531386
MonotonicityNot monotonic
2025-05-17T00:36:42.515773image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4498904
97.4%
1 13548
 
0.3%
2 8842
 
0.2%
3 4512
 
0.1%
4 3298
 
0.1%
5 2377
 
0.1%
6 1950
 
< 0.1%
7 1748
 
< 0.1%
8 1747
 
< 0.1%
10 1676
 
< 0.1%
Other values (384) 78998
 
1.7%
ValueCountFrequency (%)
0 4498904
97.4%
1 13548
 
0.3%
2 8842
 
0.2%
3 4512
 
0.1%
4 3298
 
0.1%
5 2377
 
0.1%
6 1950
 
< 0.1%
7 1748
 
< 0.1%
8 1747
 
< 0.1%
9 1608
 
< 0.1%
ValueCountFrequency (%)
5568 1
< 0.1%
3369 1
< 0.1%
577 1
< 0.1%
503 1
< 0.1%
476 1
< 0.1%
469 1
< 0.1%
460 2
< 0.1%
448 1
< 0.1%
441 1
< 0.1%
435 1
< 0.1%

station_nbr
Real number (ℝ)

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.323293
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size35.2 MiB
2025-05-17T00:36:42.562014image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q17
median12
Q315
95-th percentile18
Maximum20
Range19
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.9468843
Coefficient of variation (CV)0.43687682
Kurtosis-0.82249739
Mean11.323293
Median Absolute Deviation (MAD)4
Skewness-0.35232687
Sum52286439
Variance24.471665
MonotonicityNot monotonic
2025-05-17T00:36:42.608590image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
17 630702
13.7%
13 561105
12.2%
12 394272
 
8.5%
14 388500
 
8.4%
3 304362
 
6.6%
10 299034
 
6.5%
7 298368
 
6.5%
9 213120
 
4.6%
16 211344
 
4.6%
6 205572
 
4.5%
Other values (10) 1111221
24.1%
ValueCountFrequency (%)
1 103119
 
2.2%
2 95127
 
2.1%
3 304362
6.6%
4 102120
 
2.2%
5 94461
 
2.0%
6 205572
4.5%
7 298368
6.5%
8 109113
 
2.4%
9 213120
4.6%
10 299034
6.5%
ValueCountFrequency (%)
20 104229
 
2.3%
19 96348
 
2.1%
18 107004
 
2.3%
17 630702
13.7%
16 211344
 
4.6%
15 97458
 
2.1%
14 388500
8.4%
13 561105
12.2%
12 394272
8.5%
11 202242
 
4.4%

tmax
Text

Distinct121
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size224.6 MiB
2025-05-17T00:36:42.749228image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length3
Median length2
Mean length2.0052163
Min length1

Characters and Unicode

Total characters9259287
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row52
2nd row52
3rd row52
4th row52
5th row52
ValueCountFrequency (%)
91 111777
 
2.4%
m 111444
 
2.4%
86 110445
 
2.4%
79 106560
 
2.3%
83 103230
 
2.2%
92 103008
 
2.2%
84 102564
 
2.2%
89 102342
 
2.2%
90 100788
 
2.2%
81 100677
 
2.2%
Other values (106) 3564765
77.2%
2025-05-17T00:36:42.954215image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8 1429458
15.4%
7 1261959
13.6%
9 1193250
12.9%
6 1144521
12.4%
5 915306
9.9%
4 749361
8.1%
3 675435
7.3%
1 637140
6.9%
0 597846
6.5%
2 542346
 
5.9%
Other values (2) 112665
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9259287
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
8 1429458
15.4%
7 1261959
13.6%
9 1193250
12.9%
6 1144521
12.4%
5 915306
9.9%
4 749361
8.1%
3 675435
7.3%
1 637140
6.9%
0 597846
6.5%
2 542346
 
5.9%
Other values (2) 112665
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9259287
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
8 1429458
15.4%
7 1261959
13.6%
9 1193250
12.9%
6 1144521
12.4%
5 915306
9.9%
4 749361
8.1%
3 675435
7.3%
1 637140
6.9%
0 597846
6.5%
2 542346
 
5.9%
Other values (2) 112665
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9259287
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
8 1429458
15.4%
7 1261959
13.6%
9 1193250
12.9%
6 1144521
12.4%
5 915306
9.9%
4 749361
8.1%
3 675435
7.3%
1 637140
6.9%
0 597846
6.5%
2 542346
 
5.9%
Other values (2) 112665
 
1.2%

tmin
Text

Distinct108
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size224.4 MiB
2025-05-17T00:36:43.089544image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length3
Median length2
Mean length1.9634135
Min length1

Characters and Unicode

Total characters9066258
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row31
2nd row31
3rd row31
4th row31
5th row31
ValueCountFrequency (%)
m 111333
 
2.4%
73 110001
 
2.4%
74 107559
 
2.3%
72 107004
 
2.3%
71 101454
 
2.2%
75 96570
 
2.1%
59 95682
 
2.1%
57 88578
 
1.9%
70 85026
 
1.8%
60 84582
 
1.8%
Other values (79) 3629811
78.6%
2025-05-17T00:36:43.276734image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 1269174
14.0%
7 1268397
14.0%
6 1243089
13.7%
3 1120212
12.4%
4 1112331
12.3%
2 906204
10.0%
1 640914
7.1%
8 478854
 
5.3%
0 452103
 
5.0%
9 434787
 
4.8%
Other values (2) 140193
 
1.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9066258
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 1269174
14.0%
7 1268397
14.0%
6 1243089
13.7%
3 1120212
12.4%
4 1112331
12.3%
2 906204
10.0%
1 640914
7.1%
8 478854
 
5.3%
0 452103
 
5.0%
9 434787
 
4.8%
Other values (2) 140193
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9066258
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 1269174
14.0%
7 1268397
14.0%
6 1243089
13.7%
3 1120212
12.4%
4 1112331
12.3%
2 906204
10.0%
1 640914
7.1%
8 478854
 
5.3%
0 452103
 
5.0%
9 434787
 
4.8%
Other values (2) 140193
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9066258
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 1269174
14.0%
7 1268397
14.0%
6 1243089
13.7%
3 1120212
12.4%
4 1112331
12.3%
2 906204
10.0%
1 640914
7.1%
8 478854
 
5.3%
0 452103
 
5.0%
9 434787
 
4.8%
Other values (2) 140193
 
1.5%

tavg
Text

Distinct112
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size224.4 MiB
2025-05-17T00:36:43.409071image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length3
Median length2
Mean length1.9559615
Min length1

Characters and Unicode

Total characters9031848
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row42
2nd row42
3rd row42
4th row42
5th row42
ValueCountFrequency (%)
m 187701
 
4.1%
83 109335
 
2.4%
81 107892
 
2.3%
82 101010
 
2.2%
79 98790
 
2.1%
71 97680
 
2.1%
69 95238
 
2.1%
75 94683
 
2.1%
70 92907
 
2.0%
80 92796
 
2.0%
Other values (92) 3539568
76.7%
2025-05-17T00:36:43.594862image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7 1345986
14.9%
8 1224330
13.6%
6 1221000
13.5%
5 1145631
12.7%
4 1012209
11.2%
3 823620
9.1%
2 613386
6.8%
1 517815
 
5.7%
9 501720
 
5.6%
0 431124
 
4.8%
Other values (2) 195027
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9031848
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
7 1345986
14.9%
8 1224330
13.6%
6 1221000
13.5%
5 1145631
12.7%
4 1012209
11.2%
3 823620
9.1%
2 613386
6.8%
1 517815
 
5.7%
9 501720
 
5.6%
0 431124
 
4.8%
Other values (2) 195027
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9031848
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
7 1345986
14.9%
8 1224330
13.6%
6 1221000
13.5%
5 1145631
12.7%
4 1012209
11.2%
3 823620
9.1%
2 613386
6.8%
1 517815
 
5.7%
9 501720
 
5.6%
0 431124
 
4.8%
Other values (2) 195027
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9031848
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
7 1345986
14.9%
8 1224330
13.6%
6 1221000
13.5%
5 1145631
12.7%
4 1012209
11.2%
3 823620
9.1%
2 613386
6.8%
1 517815
 
5.7%
9 501720
 
5.6%
0 431124
 
4.8%
Other values (2) 195027
 
2.2%

depart
Text

Distinct64
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size221.8 MiB
2025-05-17T00:36:43.662908image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length3
Median length1
Mean length1.3596635
Min length1

Characters and Unicode

Total characters6278382
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowM
3rd rowM
4th rowM
5th rowM
ValueCountFrequency (%)
m 3070038
66.5%
1 166500
 
3.6%
2 164058
 
3.6%
3 163281
 
3.5%
4 140304
 
3.0%
5 127428
 
2.8%
6 114885
 
2.5%
8 90465
 
2.0%
7 89577
 
1.9%
0 84915
 
1.8%
Other values (25) 406149
 
8.8%
2025-05-17T00:36:43.875145image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
M 3070038
48.9%
763347
 
12.2%
- 562326
 
9.0%
1 527472
 
8.4%
2 238761
 
3.8%
3 198690
 
3.2%
4 178266
 
2.8%
5 157176
 
2.5%
0 153957
 
2.5%
6 139860
 
2.2%
Other values (3) 288489
 
4.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6278382
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 3070038
48.9%
763347
 
12.2%
- 562326
 
9.0%
1 527472
 
8.4%
2 238761
 
3.8%
3 198690
 
3.2%
4 178266
 
2.8%
5 157176
 
2.5%
0 153957
 
2.5%
6 139860
 
2.2%
Other values (3) 288489
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6278382
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 3070038
48.9%
763347
 
12.2%
- 562326
 
9.0%
1 527472
 
8.4%
2 238761
 
3.8%
3 198690
 
3.2%
4 178266
 
2.8%
5 157176
 
2.5%
0 153957
 
2.5%
6 139860
 
2.2%
Other values (3) 288489
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6278382
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 3070038
48.9%
763347
 
12.2%
- 562326
 
9.0%
1 527472
 
8.4%
2 238761
 
3.8%
3 198690
 
3.2%
4 178266
 
2.8%
5 157176
 
2.5%
0 153957
 
2.5%
6 139860
 
2.2%
Other values (3) 288489
 
4.6%
Distinct99
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size224.4 MiB
2025-05-17T00:36:43.997453image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length3
Median length2
Mean length1.956274
Min length1

Characters and Unicode

Total characters9033291
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row36
2nd row36
3rd row36
4th row36
5th row36
ValueCountFrequency (%)
65 112110
 
2.4%
63 109224
 
2.4%
68 107004
 
2.3%
64 106449
 
2.3%
66 101676
 
2.2%
62 101010
 
2.2%
71 100122
 
2.2%
69 98568
 
2.1%
67 98457
 
2.1%
72 96348
 
2.1%
Other values (69) 3586632
77.7%
2025-05-17T00:36:44.184629image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6 1451880
16.1%
5 1216449
13.5%
2 1088355
12.0%
3 1079364
11.9%
4 1068597
11.8%
7 873237
9.7%
1 792318
8.8%
0 453324
 
5.0%
9 441558
 
4.9%
8 440448
 
4.9%
Other values (2) 127761
 
1.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9033291
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
6 1451880
16.1%
5 1216449
13.5%
2 1088355
12.0%
3 1079364
11.9%
4 1068597
11.8%
7 873237
9.7%
1 792318
8.8%
0 453324
 
5.0%
9 441558
 
4.9%
8 440448
 
4.9%
Other values (2) 127761
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9033291
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
6 1451880
16.1%
5 1216449
13.5%
2 1088355
12.0%
3 1079364
11.9%
4 1068597
11.8%
7 873237
9.7%
1 792318
8.8%
0 453324
 
5.0%
9 441558
 
4.9%
8 440448
 
4.9%
Other values (2) 127761
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9033291
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
6 1451880
16.1%
5 1216449
13.5%
2 1088355
12.0%
3 1079364
11.9%
4 1068597
11.8%
7 873237
9.7%
1 792318
8.8%
0 453324
 
5.0%
9 441558
 
4.9%
8 440448
 
4.9%
Other values (2) 127761
 
1.4%
Distinct93
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size224.4 MiB
2025-05-17T00:36:44.309009image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length3
Median length2
Mean length1.9591587
Min length1

Characters and Unicode

Total characters9046611
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row40
2nd row40
3rd row40
4th row40
5th row40
ValueCountFrequency (%)
m 167499
 
3.6%
74 149739
 
3.2%
75 149517
 
3.2%
72 141414
 
3.1%
73 134976
 
2.9%
71 120435
 
2.6%
70 109002
 
2.4%
67 104229
 
2.3%
64 97680
 
2.1%
58 97347
 
2.1%
Other values (72) 3345762
72.5%
2025-05-17T00:36:44.486985image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7 1373958
15.2%
6 1359639
15.0%
5 1321455
14.6%
4 1215672
13.4%
3 1086024
12.0%
2 766788
8.5%
1 537906
 
5.9%
8 420579
 
4.6%
0 402375
 
4.4%
9 388056
 
4.3%
Other values (2) 174159
 
1.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9046611
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
7 1373958
15.2%
6 1359639
15.0%
5 1321455
14.6%
4 1215672
13.4%
3 1086024
12.0%
2 766788
8.5%
1 537906
 
5.9%
8 420579
 
4.6%
0 402375
 
4.4%
9 388056
 
4.3%
Other values (2) 174159
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9046611
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
7 1373958
15.2%
6 1359639
15.0%
5 1321455
14.6%
4 1215672
13.4%
3 1086024
12.0%
2 766788
8.5%
1 537906
 
5.9%
8 420579
 
4.6%
0 402375
 
4.4%
9 388056
 
4.3%
Other values (2) 174159
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9046611
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
7 1373958
15.2%
6 1359639
15.0%
5 1321455
14.6%
4 1215672
13.4%
3 1086024
12.0%
2 766788
8.5%
1 537906
 
5.9%
8 420579
 
4.6%
0 402375
 
4.4%
9 388056
 
4.3%
Other values (2) 174159
 
1.9%

heat
Text

Distinct77
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size221.7 MiB
2025-05-17T00:36:44.582090image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length2
Median length1
Mean length1.3488942
Min length1

Characters and Unicode

Total characters6228654
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row23
2nd row23
3rd row23
4th row23
5th row23
ValueCountFrequency (%)
0 2182260
47.3%
m 187701
 
4.1%
8 84249
 
1.8%
1 77700
 
1.7%
7 74481
 
1.6%
2 73926
 
1.6%
10 70707
 
1.5%
12 70374
 
1.5%
11 69597
 
1.5%
4 67599
 
1.5%
Other values (67) 1659006
35.9%
2025-05-17T00:36:44.722616image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2382948
38.3%
1 911976
 
14.6%
2 744366
 
12.0%
3 530469
 
8.5%
4 349872
 
5.6%
5 260628
 
4.2%
8 225885
 
3.6%
6 223110
 
3.6%
7 220335
 
3.5%
9 191364
 
3.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6228654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2382948
38.3%
1 911976
 
14.6%
2 744366
 
12.0%
3 530469
 
8.5%
4 349872
 
5.6%
5 260628
 
4.2%
8 225885
 
3.6%
6 223110
 
3.6%
7 220335
 
3.5%
9 191364
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6228654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2382948
38.3%
1 911976
 
14.6%
2 744366
 
12.0%
3 530469
 
8.5%
4 349872
 
5.6%
5 260628
 
4.2%
8 225885
 
3.6%
6 223110
 
3.6%
7 220335
 
3.5%
9 191364
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6228654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2382948
38.3%
1 911976
 
14.6%
2 744366
 
12.0%
3 530469
 
8.5%
4 349872
 
5.6%
5 260628
 
4.2%
8 225885
 
3.6%
6 223110
 
3.6%
7 220335
 
3.5%
9 191364
 
3.1%

cool
Categorical

Distinct37
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size224.4 MiB
0
2326227 
M
 
187701
18
 
109335
16
 
107892
17
 
101010
Other values (32)
1785435 

Length

Max length2
Median length2
Mean length1.959351
Min length1

Characters and Unicode

Total characters9047499
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row 0
2nd row 0
3rd row 0
4th row 0
5th row 0

Common Values

ValueCountFrequency (%)
0 2326227
50.4%
M 187701
 
4.1%
18 109335
 
2.4%
16 107892
 
2.3%
17 101010
 
2.2%
14 98790
 
2.1%
6 97680
 
2.1%
4 95238
 
2.1%
10 94683
 
2.1%
5 92907
 
2.0%
Other values (27) 1306137
28.3%

Length

2025-05-17T00:36:44.787487image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0 2326227
50.4%
m 187701
 
4.1%
18 109335
 
2.4%
16 107892
 
2.3%
17 101010
 
2.2%
14 98790
 
2.1%
6 97680
 
2.1%
4 95238
 
2.1%
10 94683
 
2.1%
5 92907
 
2.0%
Other values (27) 1306137
28.3%

Most occurring characters

ValueCountFrequency (%)
3136416
34.7%
0 2504271
27.7%
1 1185369
 
13.1%
2 568098
 
6.3%
3 231435
 
2.6%
4 227328
 
2.5%
6 220002
 
2.4%
5 207792
 
2.3%
7 201354
 
2.2%
8 200022
 
2.2%
Other values (2) 365412
 
4.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9047499
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3136416
34.7%
0 2504271
27.7%
1 1185369
 
13.1%
2 568098
 
6.3%
3 231435
 
2.6%
4 227328
 
2.5%
6 220002
 
2.4%
5 207792
 
2.3%
7 201354
 
2.2%
8 200022
 
2.2%
Other values (2) 365412
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9047499
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3136416
34.7%
0 2504271
27.7%
1 1185369
 
13.1%
2 568098
 
6.3%
3 231435
 
2.6%
4 227328
 
2.5%
6 220002
 
2.4%
5 207792
 
2.3%
7 201354
 
2.2%
8 200022
 
2.2%
Other values (2) 365412
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9047499
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3136416
34.7%
0 2504271
27.7%
1 1185369
 
13.1%
2 568098
 
6.3%
3 231435
 
2.6%
4 227328
 
2.5%
6 220002
 
2.4%
5 207792
 
2.3%
7 201354
 
2.2%
8 200022
 
2.2%
Other values (2) 365412
 
4.0%
Distinct219
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size225.7 MiB
2025-05-17T00:36:44.948665image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length4
Median length1
Mean length2.2584856
Min length1

Characters and Unicode

Total characters10428783
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-
2nd row-
3rd row-
4th row-
5th row-
ValueCountFrequency (%)
2680539
58.1%
0529 29415
 
0.6%
0525 23754
 
0.5%
0530 21201
 
0.5%
0526 20535
 
0.4%
0531 20424
 
0.4%
0724 20313
 
0.4%
0527 16539
 
0.4%
0528 15873
 
0.3%
0515 15762
 
0.3%
Other values (209) 1753245
38.0%
2025-05-17T00:36:45.196424image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 2680539
25.7%
0 2407923
23.1%
5 1152291
11.0%
6 886779
 
8.5%
7 621489
 
6.0%
2 604506
 
5.8%
4 584193
 
5.6%
1 550449
 
5.3%
3 541347
 
5.2%
9 208680
 
2.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10428783
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 2680539
25.7%
0 2407923
23.1%
5 1152291
11.0%
6 886779
 
8.5%
7 621489
 
6.0%
2 604506
 
5.8%
4 584193
 
5.6%
1 550449
 
5.3%
3 541347
 
5.2%
9 208680
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10428783
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 2680539
25.7%
0 2407923
23.1%
5 1152291
11.0%
6 886779
 
8.5%
7 621489
 
6.0%
2 604506
 
5.8%
4 584193
 
5.6%
1 550449
 
5.3%
3 541347
 
5.2%
9 208680
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10428783
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 2680539
25.7%
0 2407923
23.1%
5 1152291
11.0%
6 886779
 
8.5%
7 621489
 
6.0%
2 604506
 
5.8%
4 584193
 
5.6%
1 550449
 
5.3%
3 541347
 
5.2%
9 208680
 
2.0%

sunset
Text

Distinct223
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size225.7 MiB
2025-05-17T00:36:45.397076image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length4
Median length1
Mean length2.2584856
Min length1

Characters and Unicode

Total characters10428783
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-
2nd row-
3rd row-
4th row-
5th row-
ValueCountFrequency (%)
2680539
58.1%
1932 32523
 
0.7%
1928 20868
 
0.5%
1924 20202
 
0.4%
1929 19536
 
0.4%
1927 19425
 
0.4%
1936 19203
 
0.4%
1930 18870
 
0.4%
1726 18759
 
0.4%
1949 18204
 
0.4%
Other values (213) 1749471
37.9%
2025-05-17T00:36:45.647826image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 2680539
25.7%
1 2446773
23.5%
9 888000
 
8.5%
8 871572
 
8.4%
7 683094
 
6.6%
2 607725
 
5.8%
3 602730
 
5.8%
4 510933
 
4.9%
0 433011
 
4.2%
5 413364
 
4.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10428783
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 2680539
25.7%
1 2446773
23.5%
9 888000
 
8.5%
8 871572
 
8.4%
7 683094
 
6.6%
2 607725
 
5.8%
3 602730
 
5.8%
4 510933
 
4.9%
0 433011
 
4.2%
5 413364
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10428783
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 2680539
25.7%
1 2446773
23.5%
9 888000
 
8.5%
8 871572
 
8.4%
7 683094
 
6.6%
2 607725
 
5.8%
3 602730
 
5.8%
4 510933
 
4.9%
0 433011
 
4.2%
5 413364
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10428783
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 2680539
25.7%
1 2446773
23.5%
9 888000
 
8.5%
8 871572
 
8.4%
7 683094
 
6.6%
2 607725
 
5.8%
3 602730
 
5.8%
4 510933
 
4.9%
0 433011
 
4.2%
5 413364
 
4.0%
Distinct409
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size228.7 MiB
2025-05-17T00:36:45.733474image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length28
Median length1
Mean length2.9445673
Min length1

Characters and Unicode

Total characters13596834
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRA FZFG BR
2nd rowRA FZFG BR
3rd rowRA FZFG BR
4th rowRA FZFG BR
5th rowRA FZFG BR
ValueCountFrequency (%)
br 1311132
33.0%
ra 1020978
25.7%
hz 303807
 
7.7%
fg 276279
 
7.0%
tsra 251193
 
6.3%
sn 210234
 
5.3%
ts 196581
 
5.0%
vcts 115218
 
2.9%
dz 82917
 
2.1%
fzfg 49284
 
1.2%
Other values (17) 151737
 
3.8%
2025-05-17T00:36:45.861404image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4489062
33.0%
R 2609721
19.2%
B 1352868
 
9.9%
A 1298145
 
9.5%
S 791652
 
5.8%
T 564435
 
4.2%
Z 473970
 
3.5%
F 458874
 
3.4%
G 368076
 
2.7%
H 303807
 
2.2%
Other values (11) 886224
 
6.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13596834
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4489062
33.0%
R 2609721
19.2%
B 1352868
 
9.9%
A 1298145
 
9.5%
S 791652
 
5.8%
T 564435
 
4.2%
Z 473970
 
3.5%
F 458874
 
3.4%
G 368076
 
2.7%
H 303807
 
2.2%
Other values (11) 886224
 
6.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13596834
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4489062
33.0%
R 2609721
19.2%
B 1352868
 
9.9%
A 1298145
 
9.5%
S 791652
 
5.8%
T 564435
 
4.2%
Z 473970
 
3.5%
F 458874
 
3.4%
G 368076
 
2.7%
H 303807
 
2.2%
Other values (11) 886224
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13596834
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4489062
33.0%
R 2609721
19.2%
B 1352868
 
9.9%
A 1298145
 
9.5%
S 791652
 
5.8%
T 564435
 
4.2%
Z 473970
 
3.5%
F 458874
 
3.4%
G 368076
 
2.7%
H 303807
 
2.2%
Other values (11) 886224
 
6.5%
Distinct58
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size225.8 MiB
2025-05-17T00:36:45.943811image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.2825481
Min length1

Characters and Unicode

Total characters10539894
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowM
3rd rowM
4th rowM
5th rowM
ValueCountFrequency (%)
0.0 2884224
62.5%
m 1656786
35.9%
t 47730
 
1.0%
0.2 3219
 
0.1%
0.1 3108
 
0.1%
0.5 2442
 
0.1%
0.4 1776
 
< 0.1%
0.8 1665
 
< 0.1%
0.9 1665
 
< 0.1%
1.0 1443
 
< 0.1%
Other values (48) 13542
 
0.3%
2025-05-17T00:36:46.076913image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 5787651
54.9%
. 2913084
27.6%
M 1656786
 
15.7%
95460
 
0.9%
T 47730
 
0.5%
1 11211
 
0.1%
2 6660
 
0.1%
5 4329
 
< 0.1%
4 3885
 
< 0.1%
3 3885
 
< 0.1%
Other values (4) 9213
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10539894
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 5787651
54.9%
. 2913084
27.6%
M 1656786
 
15.7%
95460
 
0.9%
T 47730
 
0.5%
1 11211
 
0.1%
2 6660
 
0.1%
5 4329
 
< 0.1%
4 3885
 
< 0.1%
3 3885
 
< 0.1%
Other values (4) 9213
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10539894
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 5787651
54.9%
. 2913084
27.6%
M 1656786
 
15.7%
95460
 
0.9%
T 47730
 
0.5%
1 11211
 
0.1%
2 6660
 
0.1%
5 4329
 
< 0.1%
4 3885
 
< 0.1%
3 3885
 
< 0.1%
Other values (4) 9213
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10539894
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 5787651
54.9%
. 2913084
27.6%
M 1656786
 
15.7%
95460
 
0.9%
T 47730
 
0.5%
1 11211
 
0.1%
2 6660
 
0.1%
5 4329
 
< 0.1%
4 3885
 
< 0.1%
3 3885
 
< 0.1%
Other values (4) 9213
 
0.1%
Distinct222
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size232.6 MiB
2025-05-17T00:36:46.235913image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.8139663
Min length1

Characters and Unicode

Total characters17611371
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.05
2nd row0.05
3rd row0.05
4th row0.05
5th row0.05
ValueCountFrequency (%)
0.00 2842932
61.6%
t 562326
 
12.2%
0.01 150294
 
3.3%
m 98901
 
2.1%
0.02 81252
 
1.8%
0.03 53391
 
1.2%
0.04 44622
 
1.0%
0.05 36630
 
0.8%
0.06 34743
 
0.8%
0.07 30081
 
0.7%
Other values (212) 682428
 
14.8%
2025-05-17T00:36:46.464891image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 10164048
57.7%
. 3956373
 
22.5%
1124652
 
6.4%
T 562326
 
3.2%
1 446664
 
2.5%
2 274170
 
1.6%
3 195027
 
1.1%
4 170829
 
1.0%
5 154734
 
0.9%
6 134643
 
0.8%
Other values (4) 427905
 
2.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17611371
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 10164048
57.7%
. 3956373
 
22.5%
1124652
 
6.4%
T 562326
 
3.2%
1 446664
 
2.5%
2 274170
 
1.6%
3 195027
 
1.1%
4 170829
 
1.0%
5 154734
 
0.9%
6 134643
 
0.8%
Other values (4) 427905
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17611371
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 10164048
57.7%
. 3956373
 
22.5%
1124652
 
6.4%
T 562326
 
3.2%
1 446664
 
2.5%
2 274170
 
1.6%
3 195027
 
1.1%
4 170829
 
1.0%
5 154734
 
0.9%
6 134643
 
0.8%
Other values (4) 427905
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17611371
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 10164048
57.7%
. 3956373
 
22.5%
1124652
 
6.4%
T 562326
 
3.2%
1 446664
 
2.5%
2 274170
 
1.6%
3 195027
 
1.1%
4 170829
 
1.0%
5 154734
 
0.9%
6 134643
 
0.8%
Other values (4) 427905
 
2.4%
Distinct319
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size237.4 MiB
2025-05-17T00:36:46.808067image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.9048077
Min length1

Characters and Unicode

Total characters22648440
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row29.78
2nd row29.78
3rd row29.78
4th row29.78
5th row29.78
ValueCountFrequency (%)
m 109890
 
2.4%
29.28 61716
 
1.3%
29.26 59940
 
1.3%
29.39 58386
 
1.3%
29.41 57498
 
1.2%
29.36 55167
 
1.2%
29.31 54501
 
1.2%
29.23 53724
 
1.2%
29.21 52947
 
1.1%
29.34 52614
 
1.1%
Other values (309) 4001217
86.7%
2025-05-17T00:36:47.055262image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 4935393
21.8%
. 4507710
19.9%
9 3511485
15.5%
3 1698744
 
7.5%
8 1569096
 
6.9%
0 1470528
 
6.5%
4 1455765
 
6.4%
1 1061382
 
4.7%
6 865467
 
3.8%
5 819624
 
3.6%
Other values (2) 753246
 
3.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 22648440
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 4935393
21.8%
. 4507710
19.9%
9 3511485
15.5%
3 1698744
 
7.5%
8 1569096
 
6.9%
0 1470528
 
6.5%
4 1455765
 
6.4%
1 1061382
 
4.7%
6 865467
 
3.8%
5 819624
 
3.6%
Other values (2) 753246
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 22648440
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 4935393
21.8%
. 4507710
19.9%
9 3511485
15.5%
3 1698744
 
7.5%
8 1569096
 
6.9%
0 1470528
 
6.5%
4 1455765
 
6.4%
1 1061382
 
4.7%
6 865467
 
3.8%
5 819624
 
3.6%
Other values (2) 753246
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 22648440
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 4935393
21.8%
. 4507710
19.9%
9 3511485
15.5%
3 1698744
 
7.5%
8 1569096
 
6.9%
0 1470528
 
6.5%
4 1455765
 
6.4%
1 1061382
 
4.7%
6 865467
 
3.8%
5 819624
 
3.6%
Other values (2) 753246
 
3.3%
Distinct152
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size237.0 MiB
2025-05-17T00:36:47.229375image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.8140385
Min length1

Characters and Unicode

Total characters22229304
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row29.92
2nd row29.92
3rd row29.92
4th row29.92
5th row29.92
ValueCountFrequency (%)
m 214674
 
4.6%
30.00 109890
 
2.4%
29.94 108891
 
2.4%
29.97 107004
 
2.3%
29.96 106227
 
2.3%
29.91 105006
 
2.3%
30.06 103674
 
2.2%
30.01 100899
 
2.2%
29.95 96459
 
2.1%
29.99 96126
 
2.1%
Other values (142) 3468750
75.1%
2025-05-17T00:36:47.459824image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 4402926
19.8%
0 3683535
16.6%
9 3514260
15.8%
3 2932065
13.2%
2 2907534
13.1%
1 1144743
 
5.1%
8 1052391
 
4.7%
7 730158
 
3.3%
6 586968
 
2.6%
4 537240
 
2.4%
Other values (2) 737484
 
3.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 22229304
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 4402926
19.8%
0 3683535
16.6%
9 3514260
15.8%
3 2932065
13.2%
2 2907534
13.1%
1 1144743
 
5.1%
8 1052391
 
4.7%
7 730158
 
3.3%
6 586968
 
2.6%
4 537240
 
2.4%
Other values (2) 737484
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 22229304
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 4402926
19.8%
0 3683535
16.6%
9 3514260
15.8%
3 2932065
13.2%
2 2907534
13.1%
1 1144743
 
5.1%
8 1052391
 
4.7%
7 730158
 
3.3%
6 586968
 
2.6%
4 537240
 
2.4%
Other values (2) 737484
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 22229304
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 4402926
19.8%
0 3683535
16.6%
9 3514260
15.8%
3 2932065
13.2%
2 2907534
13.1%
1 1144743
 
5.1%
8 1052391
 
4.7%
7 730158
 
3.3%
6 586968
 
2.6%
4 537240
 
2.4%
Other values (2) 737484
 
3.3%
Distinct246
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size233.2 MiB
2025-05-17T00:36:47.684802image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.9534135
Min length1

Characters and Unicode

Total characters18255282
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row 3.6
2nd row 3.6
3rd row 3.6
4th row 3.6
5th row 3.6
ValueCountFrequency (%)
m 75480
 
1.6%
4.1 56832
 
1.2%
3.1 55167
 
1.2%
2.1 53724
 
1.2%
3.5 53391
 
1.2%
4.3 52281
 
1.1%
2.8 52281
 
1.1%
3.7 51393
 
1.1%
2.9 50949
 
1.1%
2.7 50616
 
1.1%
Other values (235) 4065486
88.0%
2025-05-17T00:36:47.955797image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 4542120
24.9%
3698631
20.3%
1 1829946
10.0%
2 1086579
 
6.0%
3 1046619
 
5.7%
4 1033743
 
5.7%
5 929736
 
5.1%
6 878343
 
4.8%
0 823287
 
4.5%
7 815517
 
4.5%
Other values (3) 1570761
 
8.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18255282
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 4542120
24.9%
3698631
20.3%
1 1829946
10.0%
2 1086579
 
6.0%
3 1046619
 
5.7%
4 1033743
 
5.7%
5 929736
 
5.1%
6 878343
 
4.8%
0 823287
 
4.5%
7 815517
 
4.5%
Other values (3) 1570761
 
8.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18255282
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 4542120
24.9%
3698631
20.3%
1 1829946
10.0%
2 1086579
 
6.0%
3 1046619
 
5.7%
4 1033743
 
5.7%
5 929736
 
5.1%
6 878343
 
4.8%
0 823287
 
4.5%
7 815517
 
4.5%
Other values (3) 1570761
 
8.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18255282
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 4542120
24.9%
3698631
20.3%
1 1829946
10.0%
2 1086579
 
6.0%
3 1046619
 
5.7%
4 1033743
 
5.7%
5 929736
 
5.1%
6 878343
 
4.8%
0 823287
 
4.5%
7 815517
 
4.5%
Other values (3) 1570761
 
8.6%

resultdir
Categorical

Distinct37
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size224.5 MiB
17
354090 
18
337995 
16
 
265179
19
 
233988
15
 
199023
Other values (32)
3227325 

Length

Max length2
Median length2
Mean length1.9836538
Min length1

Characters and Unicode

Total characters9159720
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20
2nd row20
3rd row20
4th row20
5th row20

Common Values

ValueCountFrequency (%)
17 354090
 
7.7%
18 337995
 
7.3%
16 265179
 
5.7%
19 233988
 
5.1%
15 199023
 
4.3%
01 170274
 
3.7%
20 162726
 
3.5%
02 159951
 
3.5%
36 152403
 
3.3%
14 148185
 
3.2%
Other values (27) 2433786
52.7%

Length

2025-05-17T00:36:48.029713image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
17 354090
 
7.7%
18 337995
 
7.3%
16 265179
 
5.7%
19 233988
 
5.1%
15 199023
 
4.3%
01 170274
 
3.7%
20 162726
 
3.5%
02 159951
 
3.5%
36 152403
 
3.3%
14 148185
 
3.2%
Other values (27) 2433786
52.7%

Most occurring characters

ValueCountFrequency (%)
1 2367519
25.8%
2 1452768
15.9%
3 1218891
13.3%
0 1205460
13.2%
6 551892
 
6.0%
5 500943
 
5.5%
7 496614
 
5.4%
8 478632
 
5.2%
4 431790
 
4.7%
9 379731
 
4.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9159720
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 2367519
25.8%
2 1452768
15.9%
3 1218891
13.3%
0 1205460
13.2%
6 551892
 
6.0%
5 500943
 
5.5%
7 496614
 
5.4%
8 478632
 
5.2%
4 431790
 
4.7%
9 379731
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9159720
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 2367519
25.8%
2 1452768
15.9%
3 1218891
13.3%
0 1205460
13.2%
6 551892
 
6.0%
5 500943
 
5.5%
7 496614
 
5.4%
8 478632
 
5.2%
4 431790
 
4.7%
9 379731
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9159720
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 2367519
25.8%
2 1452768
15.9%
3 1218891
13.3%
0 1205460
13.2%
6 551892
 
6.0%
5 500943
 
5.5%
7 496614
 
5.4%
8 478632
 
5.2%
4 431790
 
4.7%
9 379731
 
4.1%
Distinct254
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size229.9 MiB
2025-05-17T00:36:48.209693image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.2115625
Min length1

Characters and Unicode

Total characters14829711
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.6
2nd row4.6
3rd row4.6
4th row4.6
5th row4.6
ValueCountFrequency (%)
m 101454
 
2.2%
6.9 82029
 
1.8%
8.1 74592
 
1.6%
5.8 74259
 
1.6%
4.6 61827
 
1.3%
6.6 58497
 
1.3%
6.5 58053
 
1.3%
7.3 56277
 
1.2%
7.4 56055
 
1.2%
7.6 54390
 
1.2%
Other values (244) 3940167
85.3%
2025-05-17T00:36:48.477998image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 4516146
30.5%
1 1882893
12.7%
6 1064601
 
7.2%
5 1060494
 
7.2%
4 999777
 
6.7%
7 993561
 
6.7%
8 927516
 
6.3%
3 869463
 
5.9%
2 864024
 
5.8%
9 820068
 
5.5%
Other values (2) 831168
 
5.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14829711
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 4516146
30.5%
1 1882893
12.7%
6 1064601
 
7.2%
5 1060494
 
7.2%
4 999777
 
6.7%
7 993561
 
6.7%
8 927516
 
6.3%
3 869463
 
5.9%
2 864024
 
5.8%
9 820068
 
5.5%
Other values (2) 831168
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14829711
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 4516146
30.5%
1 1882893
12.7%
6 1064601
 
7.2%
5 1060494
 
7.2%
4 999777
 
6.7%
7 993561
 
6.7%
8 927516
 
6.3%
3 869463
 
5.9%
2 864024
 
5.8%
9 820068
 
5.5%
Other values (2) 831168
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14829711
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 4516146
30.5%
1 1882893
12.7%
6 1064601
 
7.2%
5 1060494
 
7.2%
4 999777
 
6.7%
7 993561
 
6.7%
8 927516
 
6.3%
3 869463
 
5.9%
2 864024
 
5.8%
9 820068
 
5.5%
Other values (2) 831168
 
5.6%

Interactions

2025-05-17T00:36:23.925872image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-17T00:36:20.748993image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-17T00:36:21.834385image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-17T00:36:22.806245image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-17T00:36:24.168821image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-17T00:36:21.078474image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-17T00:36:22.065903image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-17T00:36:23.043392image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-17T00:36:24.416235image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-17T00:36:21.335450image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-17T00:36:22.315675image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-17T00:36:23.270362image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-17T00:36:24.655118image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-17T00:36:21.583840image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-17T00:36:22.560036image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-17T00:36:23.679606image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-05-17T00:36:48.536326image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
coolitem_nbrresultdirstation_nbrstore_nbrunits
cool1.0000.0000.1100.1840.1230.000
item_nbr0.0001.0000.0000.0000.000-0.098
resultdir0.1100.0001.0000.1920.1150.000
station_nbr0.1840.0000.1921.0000.211-0.001
store_nbr0.1230.0000.1150.2111.000-0.003
units0.000-0.0980.000-0.001-0.0031.000

Missing values

2025-05-17T00:36:26.129939image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-05-17T00:36:30.552830image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

datestore_nbritem_nbrunitsstation_nbrtmaxtmintavgdepartdewpointwetbulbheatcoolsunrisesunsetcodesumsnowfallpreciptotalstnpressuresealevelresultspeedresultdiravgspeed
02012-01-011101523142M3640230--RA FZFG BRM0.0529.7829.923.6204.6
12012-01-011201523142M3640230--RA FZFG BRM0.0529.7829.923.6204.6
22012-01-011301523142M3640230--RA FZFG BRM0.0529.7829.923.6204.6
32012-01-011401523142M3640230--RA FZFG BRM0.0529.7829.923.6204.6
42012-01-011501523142M3640230--RA FZFG BRM0.0529.7829.923.6204.6
52012-01-011601523142M3640230--RA FZFG BRM0.0529.7829.923.6204.6
62012-01-011701523142M3640230--RA FZFG BRM0.0529.7829.923.6204.6
72012-01-011801523142M3640230--RA FZFG BRM0.0529.7829.923.6204.6
82012-01-0119291523142M3640230--RA FZFG BRM0.0529.7829.923.6204.6
92012-01-0111001523142M3640230--RA FZFG BRM0.0529.7829.923.6204.6
datestore_nbritem_nbrunitsstation_nbrtmaxtmintavgdepartdewpointwetbulbheatcoolsunrisesunsetcodesumsnowfallpreciptotalstnpressuresealevelresultspeedresultdiravgspeed
46175902014-10-3145102016533444M3541210--M0.0029.9029.994.5035.3
46175912014-10-3145103016533444M3541210--M0.0029.9029.994.5035.3
46175922014-10-3145104016533444M3541210--M0.0029.9029.994.5035.3
46175932014-10-3145105016533444M3541210--M0.0029.9029.994.5035.3
46175942014-10-3145106016533444M3541210--M0.0029.9029.994.5035.3
46175952014-10-3145107016533444M3541210--M0.0029.9029.994.5035.3
46175962014-10-3145108016533444M3541210--M0.0029.9029.994.5035.3
46175972014-10-3145109016533444M3541210--M0.0029.9029.994.5035.3
46175982014-10-3145110016533444M3541210--M0.0029.9029.994.5035.3
46175992014-10-3145111016533444M3541210--M0.0029.9029.994.5035.3